The Microwave Infrared Combined Rainfall Algorithm (MICRA) consists in a statistical integration method using the satellite microwave-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne infrared measurements on limited sub-regions and time windows. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The actual implementation is explained, although the basic concepts of MICRA are very general and the method is easy to be extended for considering innovative statistical techniques or measurements from additional space-borne platforms. In order to demonstrate the potentiality of MICRA, case studies over central Italy are also discussed. Finally, preliminary results of MICRA validation by ground based remote and in situ measurements are shown and a comparison with a Neural Network (NN) based technique is briefly illustrated.

Satellite radiometric remote sensing of rainfall fields: multi-sensor retrieval techniques at geostationary scale

D Cimini;V Levizzani;
2005

Abstract

The Microwave Infrared Combined Rainfall Algorithm (MICRA) consists in a statistical integration method using the satellite microwave-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne infrared measurements on limited sub-regions and time windows. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The actual implementation is explained, although the basic concepts of MICRA are very general and the method is easy to be extended for considering innovative statistical techniques or measurements from additional space-borne platforms. In order to demonstrate the potentiality of MICRA, case studies over central Italy are also discussed. Finally, preliminary results of MICRA validation by ground based remote and in situ measurements are shown and a comparison with a Neural Network (NN) based technique is briefly illustrated.
2005
Istituto di Scienze dell'Atmosfera e del Clima - ISAC
Dipartimento di Scienze del Sistema Terra e Tecnologie per l'Ambiente - DSSTTA
Inglese
2
267
272
http://www.adv-geosci.net/2/267/2005/adgeo-2-267-2005.html
Sì, ma tipo non specificato
asatellite
precipitation
7
info:eu-repo/semantics/article
262
S Marzano, F; Cimini, D; Coppola, E; Verdecchia, M; Levizzani, V; Tapiador, F; J Turk, F
01 Contributo su Rivista::01.01 Articolo in rivista
none
   Multi-sensor precipitation measurements integration, calibration and flood forecasting (MUSIC)
   MUSIC
   FP5
   EVK1-CT-2000-00058

   European satellite rainfall analysis and monitoring at the geostationary scale (EURAINSAT)
   EURAINSAT
   FP5
   EVG1-CT-2000-00030
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/66938
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